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2023 Congress in Computer science, Computer Engineering, and Applied computing, CSCE 2023

作者： Hoang, Kyler Deligiannidis, Leonidas School of Engineering Wentworth Institute of Technology BostonMA02115 United States School of Computing and Data Science Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9798350327595

To combat the rising population of ADHD and dyslexic patients, current advancements in Unity and Oculus Integration are investigated and employed to design a user-friendly reading experience. Unlike physical novels that lack visual aid, our prototype ensures a distraction-free and minimalistic VR environment. It utilizes *** speech recognition to aid ADHD and dyslexic patients in mechanical and comprehensive reading. Users can also change the background color and music to enhance their facilitated experience. This approach allows the user to constantly focus on the text without averting their gaze. We believe that further development of this prototype can create a powerful tool that redefines reading E-books. © 2023 IEEE.

关键词： ADHD Dyslexia Virtual Reality

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S2AC: ENERGY-BASED REINFORCEMENT LEARNING WITH STEIN SOFT ACTOR CRITIC 12

S2AC: ENERGY-BASED REINFORCEMENT LEARNING WITH STEIN SOFT AC...

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12th International Conference on Learning Representations, ICLR 2024

作者： Messaoud, Safa Mokeddem, Billel Xue, Zhenghai Pang, Linsey An, Bo Chen, Haipeng Chawla, Sanjay Qatar Computing Research Institute Hamad Bin Khalifa University Qatar School of Computer Science and Engineering Nanyang Technological University Singapore SalesForce United States Skywork AI Data Science William & Mary United States

Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy Reinforcement Learning (MaxEnt RL), the policy is modeled as an expressive Energy-Based Model (EBM) over the Q-values. However, this formulation requires the estimation of the entropy of such EBMs, which is an open problem. To address this, previous MaxEnt RL methods either implicitly estimate the entropy, resulting in high computational complexity and variance (SQL), or follow a variational inference procedure that fits simplified actor distributions (e.g., Gaussian) for tractability (SAC). We propose Stein Soft Actor-Critic (S2AC), a MaxEnt RL algorithm that learns expressive policies without compromising efficiency. Specifically, S2AC uses parameterized Stein Variational Gradient Descent (SVGD) as the underlying policy. We derive a closed-form expression of the entropy of such policies. Our formula is computationally efficient and only depends on first-order derivatives and vector products. Empirical results show that S2AC yields more optimal solutions to the MaxEnt objective than SQL and SAC in the multi-goal environment, and outperforms SAC and SQL on the MuJoCo benchmark. Our code is available at: https://***/SafaMessaoud/S2AC-Energy-Based-RL-with-Stein-Soft-Actor-Critic. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables

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IEEE Transactions on Services computing 2023年第5期16卷 3604-3620页

作者： Zheng, Yifeng Xu, Shuangqing Wang, Songlei Gao, Yansong Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China Csiro Data61 SydneyNSW2720 Australia

Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically partitioned datasets. Gradient boosting has been popularly adopted in VFL, which builds an ensemble of weak learners (typically decision trees) to achieve promising prediction performance. Recently there have been growing interests in using decision table as an intriguing alternative weak learner in gradient boosting, due to its simpler structure, good interpretability, and promising performance. In the literature, there have been works on privacy-preserving VFL for gradient boosted decision trees, but no prior work has been devoted to the emerging case of decision tables. Training and inference on decision tables are different from that in the case of generic decision trees, not to mention gradient boosting with decision tables in VFL. In light of this, we design, implement, and evaluate Privet, the first system framework enabling privacy-preserving VFL service for gradient boosted decision tables. Privet delicately builds on lightweight cryptography and allows an arbitrary number of participants holding vertically partitioned datasets to securely train gradient boosted decision tables. Extensive experiments over several real-world datasets and synthetic datasets demonstrate that Privet achieves promising performance, with utility comparable to plaintext centralized learning. © 2008-2012 IEEE.

关键词： Decision trees

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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

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Computer Modeling in Engineering & sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

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Long Short-Term Memory (LSTM) model for Indian sign language recognition

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11185-11203页

作者： Nihalani, Rahul Chouhan, Siddharth Singh Mittal, Devansh Vadula, Jai Thakur, Shwetank Chakraborty, Sandeepan Patel, Rajneesh Kumar Singh, Uday Pratap Ghosh, Rajdeep Singh, Pritpal Saxena, Akash School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore India Department of Mathematics Central University of Jammu UT of JK India Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer India School of Engineering and Technology Central University of Haryana Haryana Mahendragarh India

The human-computer interaction process is a vital task in attaining artificial intelligence, especially for a person suffering from hearing or speaking disabilities. Recognizing actions more traditionally known as sign language is a common way for them to interact. Computer vision and Deep learning models are capable of understanding these actions and can simulate them to build up a sustainable learning process. This sign language mechanism will be helpful for both the persons with disabilities and the machines to unbound the gap to achieve intelligence. Therefore, in the proposed work, a real-Time sign language system is introduced that is capable of identifying numbers ranging from 0 to 9. The database is acquired from the 8 different subjects respectively and processed to achieve approximately 200k amount of data. Further, a deep learning model named LSTM is used for sign recognition. The results were compared with different approaches and on distinct databases proving the supremacy of the proposed work with 91.50% accuracy. Collection of daily life useful signs and further improving the efficiency of the LSTM model is the research direction for future work. The code and data will be available at https://***/rahuln2002/Sign-Language-Recognition-using-LSTM-model. © 2024-IOS Press. All rights reserved.

关键词： Long short-term memory

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Text Reasoning Chain Extraction for Multi-Hop Question Answering

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Tsinghua science and Technology 2024年第4期29卷 959-970页

作者： Pengming Wang Zijiang Zhu Qing Chen Weihuang Dai School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou 325035China School of Computer Science and also with Institute of Intelligent Information ProcessingSouth China Business CollegeGuangdong University of Foreign StudiesGuangzhou 510545China School of Psychology Jiangxi Normal UniversityNanchang 330022China also with School of Science East China Jiaotong UniversityNanchang 330013China Institute of Intelligent Information Processing South China Business CollegeGuangdong University of Foreign StudiesGuangzhou 510545China

With the advent of the information age, it will be more troublesome to search for a lot of relevant knowledge to find the information you need. Text reasoning is a very basic and important part of multi-hop question and answer tasks. This paper aims to study the integrity, uniformity, and speed of computational intelligence inference data capabilities. That is why multi-hop reasoning came into being, but it is still in its infancy, that is, it is far from enough to conduct multi-hop question and answer questions, such as search breadth, process complexity, response speed, comprehensiveness of information, etc. This paper makes a text comparison between traditional information retrieval and computational intelligence through corpus relevancy and other computing methods. The study finds that in the face of multi-hop question and answer reasoning, the reasoning data that traditional retrieval methods lagged behind in intelligence are about 35% worse. It shows that computational intelligence would be more complete, unified, and faster than traditional retrieval methods. This paper also introduces the relevant points of text reasoning and describes the process of the multi-hop question answering system, as well as the subsequent discussions and expectations.

关键词： intelligent computing multi-hop quiz text reasoning document retrieval text complex network

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Sensitivity Analysis of CoVaR 19

Sensitivity Analysis of CoVaR

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19th IEEE International Conference on Automation science and Engineering, CASE 2023

作者： Fu, Han Hong, L. Jeff Jiang, Guangxin School of Data Science Fudan University Shanghai200433 China School of Management School of Data Science Fudan University Shanghai200433 China School of Management Harbin Institute of Technology Heilongjiang Harbin150001 China

ISBN: (纸本)9798350320695

CoVaR is an important measure for assessing the systemic risk of a network composed of many systems. To optimize and control the systemic risk of the network, we need to know the sensitivity of CoVaR. In this paper, we derive closed-form expressions of the CoVaR sensitivities and design batched estimators using the infinitesimal perturbation analysis (IPA) and finite-difference methods. We establish the consistency and asymptotic normality of the proposed estimators and show that the convergence rate of the estimators is strictly slower than n-1/6. Numerical experiments show the effectiveness of our estimator and support the theoretical results. © 2023 IEEE.

关键词： Sensitivity analysis

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Packet Scheduling in Rechargeable Wireless Sensor Networks under SINR Model

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China Communications 2023年第3期20卷 286-301页

作者： Baogui Huang Jiguo Yu Chunmei Ma Guangshun Li Anming Dong School of Computer Science Qufu Normal UniversityRizhao 276826China Big Data Institute Qilu University of TechnologyJinan 250353China School of Computer Science and Technology Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio *** allocate different transmission slots to conflicting packets and overcome the challenges caused by the fact that the channel state changes quickly and is *** first algorithm proposes a prioritybased framework for packet scheduling in rechargeable sensor *** packet is assigned a priority related to the transmission delay and the remaining energy of rechargeable batteries,and the packets with higher priority are scheduled *** second algorithm mainly focuses on the energy efficiency of *** priorities are related to the transmission distance of packets,and the packets with short transmission distance are scheduled *** sensors are equipped with low-capacity rechargeable batteries,and the harvest-store-use model is *** consider imperfect *** is,the battery capacity is limited,and battery energy leaks over *** energy harvesting rate,energy retention rate and transmission power are *** simulation results indicate that the battery capacity has little effect on the packet scheduling ***,the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.

关键词： packet scheduling physical interference model rechargeable sensor networks SINR model

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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile computing 2025年第7期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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Edge computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

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Computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

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